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Grade BproductionSource date Feb 11, 2026· Last verified Oct 17, 2026

How many customer queries does Air India's AI agent handle?

Air India · Transportation & Logistics

Header image: A modern airport hall with self-service kiosks

Air India's AI.g agent, built on Azure OpenAI, answers customer questions such as baggage and pet policies. Microsoft reports it has resolved more than 13 million conversations with a 97% success rate, with only 3% of queries escalated to a human agent.

Reported outcomes

conversations resolved
more than 13 million, 97% success rate
Source [1] · self-reported
escalation
only 3% of queries escalated to a human
Source [1] · self-reported

Outcomes are reported exactly as stated by the source; we have not independently audited them.

How it works

  1. Customers ask AI.g questions in natural language.
  2. The agent reasons over Air India policy content to answer (e.g., recognizing a Labrador falls under the pet policy).
  3. Complex or unresolved queries escalate to human contact-center staff.
  4. Air India is trialing agents for full workflows such as refund processing (per source).

Risks & compliance

  • Consumer-protection rules on refunds and compensation
Build this yourself

Tools mentioned & how to build it

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Tools mentioned

Build a version yourself

  • n8n
    Open-source workflow automation with AI agent nodes; self-host or cloud.
  • Notion
    Docs and wiki with Notion AI; a home for your agent playbooks and SOPs.
  • HubSpot (Breeze agents)
    SMB-friendly CRM with Breeze AI agents for prospecting and support.

All tools by category →

Sources

[1]Microsoft· vendor customer story· Feb 11, 2026
“Since launch, it has resolved more than 13 million conversations with a 97% success rate.”
How Azure AI helped Air India reinvent customer service by answering 40,000 daily queries instantly

Grade rationale: Named organization with quantified outcomes on the vendor's customer-story page. Automated check on Oct 17, 2026: 1/1 quotes and 2/2 outcome claims matched the source. Compiled by the Agent Casefile research desk (AI-assisted; see methodology). Spot an error? Request a correction.

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